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## ECG SIGNAL RECOGNIZATION AND APPLICAITIONS

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**Anatomy Revisited**• RCA • right ventricle • inferior wall of LV • posterior wall of LV (75%) • SA Node (60%) • AV Node (>80%) • LCA • septal wall of LV • anterior wall of LV • lateral wall of LV • posterior wall of LV (10%)**Unipolar Leads**• 1 positive electrode & 1 negative “reference point” • calculated by using summation of 2 negative leads • Augmented Limb Leads • aVR, aVF, aVL • view from a vertical plane • Precordial or Chest Leads • V1-V6 • view from a horizontal plane**Waveform Components: R Wave**First positive deflection; R wave includes the downstroke returning to the baseline**Waveform Components: Q Wave**First negative deflection before R wave; Q wave includes the negative downstroke & return to baseline**Waveform Components:S Wave**Negative deflection following the R wave; S wave includes departure from & return to baseline**Waveform Components:QRS**• Q waves • Can occur normally in several leads • Normal Q waves called physiologic • Physiologic Q waves • < .04 sec (40ms) • Pathologic Q • >.04 sec (40 ms)**Waveform Components:QRS**• Q wave • Measure width • Pathologic if greater than or equal to 0.04 seconds (1 small box)**Waveform Components:QS Complex**Entire complex is negatively deflected; No R wave present**Waveform Components:J-Point**Junction between end of QRS and beginning of ST segment; Where QRS stops & makes a sudden sharp change of direction**Waveform Components: ST Segment**Segment between J-point and beginning of T wave**Lead Groups**I aVR V1 V4 II aVL V2 V5 III aVF V3 V6 Limb Leads Chest Leads**Review of Leads**• EKG Leads • EKG machines record the electrical activity. • Precordial leads or chest leads [ V1, V2, V3, V4, V5, V6 ] view the hearts horizontal plane • The heart acts as a central point of the cross section and the electrical current flows from the central point out to each of the V leads Understanding 12 Lead EKG**12-Lead View in perspectives**Axis Deviation Bundle Branch Blocks Understanding 12 Lead EKGS**Arrhythmias**• Sinus Rhythms • Premature Beats • Supraventricular Arrhythmias • Ventricular Arrhythmias • AV Junctional Blocks**Rhythm #1**• Rate? 30 bpm • Regularity? regular • P waves? normal • PR interval? 0.12 s • QRS duration? 0.10 s Interpretation? Sinus Bradycardia**Sinus Bradycardia**• Deviation from NSR - Rate < 60 bpm**Rhythm #2**• Rate? 130 bpm • Regularity? regular • P waves? normal • PR interval? 0.16 s • QRS duration? 0.08 s Interpretation? Sinus Tachycardia**Sinus Tachycardia**• Deviation from NSR - Rate > 100 bpm**Premature Beats**• Premature Atrial Contractions(PACs) • Premature Ventricular Contractions(PVCs)**Rhythm #3**• Rate? 70 bpm • Regularity? occasionally irreg. • P waves? 2/7 different contour • PR interval? 0.14 s (except 2/7) • QRS duration? 0.08 s Interpretation? NSR with Premature Atrial Contractions**Premature Atrial Contractions**• Deviation from NSR • These ectopic beats originate in the atria (but not in the SA node), therefore the contour of the P wave, the PR interval, and the timing are different than a normally generated pulse from the SA node.**Rhythm #4**• Rate? 60 bpm • Regularity? occasionally irreg. • P waves? none for 7th QRS • PR interval? 0.14 s • QRS duration? 0.08 s (7th wide) Interpretation? Sinus Rhythm with 1 PVC**PVCs**• Deviation from NSR • Ectopic beats originate in the ventricles resulting in wide and bizarre QRS complexes. • When there are more than 1 premature beats and look alike, they are called “uniform”. When they look different, they are called “multiform”.**Ventricular Conduction**Normal Signal moves rapidly through the ventricles Abnormal Signal moves slowly through the ventricles**Wisdom**(Knowledge + experience) Knowledge (Information + rules) Information (Data + context) Data The Data Pyramid Value How can we improve it ? What made it that unsuccessful ? Volume What was the lowest selling product ? How many units were sold of each product line ?**Data Mining Functions**Clustering into ‘natural’ groups (unsupervised) Classification into known classes;e.g. diagnosis (supervised) Detection of associations; e.g. in basket analysis: ”70% of customers buying bread also buy milk” Detection of sequential temporal patterns;e.g. disease development Prediction or estimation of an outcome Time series forecasting**Data Mining Techniques**(box of tricks) Statistics Linear Regression Visualization Cluster analysis Older, Data preparation, Exploratory Decision trees Rule induction Neural networks Abductive networks Newer, Modeling, Knowledge Representation**Data-based Predictive Modeling**Develop Model WithKnown Cases Use Model For New Cases 1 2 IN OUT IN OUT F(X) Attributes, X Diagnosis, Y Rock Properties Attributes (X) Diagnosis (Y) Y = F(X) Determine F(X)**Data-based Predictive Modeling by**supervised Machine learning • Database of solved examples (input-output) • Preparation: cleanup, transform, add new attributes... • Split data into a training and a test set • Training: Develop model on the training set • Evaluation: See how the model fares on the test set • Actual use: Use successful model on new input data to estimate unknown output**S**S S The Neural Network (NN) Approach HiddenLayer Input Layer Output Layer Neurons .6 Age 34 Actual: 0.65 .4 .2 0.60 .5 .1 Gender 2 .2 .3 .8 .7 4 .2 Stage Error: 0.05 Transfer Function Weights Weights Dependent Output Variable Independent Input Variables (Attributes) Error back-propagation**Medicine revolves on**Pattern Recognition, Classification, and Prediction Diagnosis: Recognize and classify patterns in multivariate patient attributes Therapy: Select from available treatment methods; based on effectiveness, suitability to patient, etc. Prognosis: Predict future outcomes based on previous experience and present conditions**Need for Data Mining in Medicine**Nature of medical data: noisy, incomplete, uncertain, nonlinearities, fuzziness Soft computing Too much data now collected due to computerization (text, graphs, images,…) Too many disease markers (attributes) now available for decision making Increased demand for health services: (Greater awareness, increased life expectancy, …) - Overworked physicians and facilities Stressful work conditions in ICUs, etc.**Medical Applications**• Screening • Diagnosis • Therapy • Prognosis • Monitoring • Biomedical/Biological Analysis • Epidemiological Studies • Hospital Management • Medical Instruction and Training**Diagnosis and Classification**• Assist in decision making with a large number of inputs and in stressful situations • Can perform automated analysis of: - Pathological signals (ECG, EEG, EMG) - Medical images (mammograms, ultrasound, X-ray, CT, and MRI) • Examples: - Heart attacks, Chest pains, Rheumatic disorders - Myocardial ischemia using the ST-T ECG complex - Coronary artery disease using SPECT images**Diagnosis and Classification ECG Interpretation**R-R interval SV tachycardia QRS amplitude QRS duration V entricular tachycardia AVF lead L V hypertrophy R V hypertrophy S-T elevation Myocardial infarction P-R interval**Outline**Biological Problem Heart Physiology ECG ventricular repolarization Simultaneously ventricular activation (depolarization) Sequential atrial activation (depolarization) After depolarizations in the ventricles**Outline**Biological Problem Difference In Wave Shape And Frequency : ECG wave shape characterization REGULAR RHYTHM Normal IRREGULAR RHYTHM Arrhythmia P ,T AND U WAVE INDISTINCT. IRREGULAR RHYTHM Ventricular Arrhythmia REGULARRHYTHM Bradycardia**Outline**The Algorithm: time domain statistics**d range**Signal derivative in initial condition point Number of Samples for Trajectors Minimum Distance between Trajectories 0 Signal derivative at the starting point Number of couples of trajectories Three Initial Conditions d0 range Outline Minimum Distance between trajectories The Algorithm Input Parameters**d 2**d 1 d 3 d Totale Discrete Map #1 Discrete Map #2 Discrete Map #3 j j j j Total Matrix of Difference Matrix of Difference #2 Matrix of Difference #1 Matrix of Difference #3 Outline The Algorithm From Discrete Map to dj**Outline**Parametric Study Initial Condition In P-wave choose the points in order to extractcoherent trajectories**Outline**Parametric Study Extraction of dj parameters From points in P-wave extract dj that have asymptotic behaviour and present limited oscillation**d**j Results Results Trend of dj dj have a similar trend for the three cases but with different value. Normal Arrhythmia Ventricular Arrhythmia Initial Slope**Ventricular**Arrhythmia Best proportionality between |d∞ | and λ Arrhythmia Normal Results Results (d∞ - λMAX) vs Power2 | |**Operator Dependent**Neural Network for P-wave recognition Automatic search of initial conditions Algoritm of Automatic clustering for 3D graphics Initial conditions obtained by visual inspectionon the P-wave 2 1 Outline Future Development Possible Solution**The study of the d∞ and the LyapunovExponent are performed**simultaneously Outline Conclusions The asymptotic distance between trajectories,d∞, has been obtained from computation ofdj dj trend is similar to one reported in literature on Chaotic System Need more medical statistics and inputs! Theoretical study Application healthy Biomedical Application: Automatic Diagnostic unhealthy**Attribute Selection: Information Gain**• Select the attribute with the highest information gain • Let pi be the probability that an arbitrary tuple in D belongs to class Ci, estimated by |Ci, D|/|D • Expected information (entropy) needed to classify a tuple in D: • Information needed (after using A to split D into v partitions) to classify D: • Information gained by branching on attribute A